Challenge: Simulate Test Coverage Tracking
Understanding and monitoring test coverage is a crucial aspect of quality assurance, as it helps you identify which parts of the codebase have been exercised by your tests and which have not. By simulating test coverage tracking, you can gain practical insights into how coverage tools operate and why they are valuable for continuous QA improvement. In this challenge, you will create a Python script that maintains a set of function names as they are "called" during test execution, then reports which functions from a predefined list were covered and which were not. This hands-on exercise reinforces the importance of thorough testing and the value of coverage metrics in real-world QA workflows.
Swipe to start coding
Write a Python function to simulate test coverage tracking for a list of functions. You are provided with a list of all function names that should be tested, and a list of function names that were actually called during test execution.
- Identify which function names from
functions_to_testare present intested_functions. - Identify which function names from
functions_to_testare not present intested_functions. - Prepare a string listing the covered functions, separated by commas, in sorted order.
- Prepare a string listing the untested functions, separated by commas, in sorted order.
- Print the covered functions with the label "Tested functions:".
- Print the untested functions with the label "Untested functions:".
Solução
Obrigado pelo seu feedback!
single
Pergunte à IA
Pergunte à IA
Pergunte o que quiser ou experimente uma das perguntas sugeridas para iniciar nosso bate-papo
Can you explain how to simulate function calls for coverage tracking?
What should the predefined list of functions look like?
How do I report which functions were covered and which were not?
Incrível!
Completion taxa melhorada para 4.76Challenge: Simulate Test Coverage Tracking
Deslize para mostrar o menu
Understanding and monitoring test coverage is a crucial aspect of quality assurance, as it helps you identify which parts of the codebase have been exercised by your tests and which have not. By simulating test coverage tracking, you can gain practical insights into how coverage tools operate and why they are valuable for continuous QA improvement. In this challenge, you will create a Python script that maintains a set of function names as they are "called" during test execution, then reports which functions from a predefined list were covered and which were not. This hands-on exercise reinforces the importance of thorough testing and the value of coverage metrics in real-world QA workflows.
Swipe to start coding
Write a Python function to simulate test coverage tracking for a list of functions. You are provided with a list of all function names that should be tested, and a list of function names that were actually called during test execution.
- Identify which function names from
functions_to_testare present intested_functions. - Identify which function names from
functions_to_testare not present intested_functions. - Prepare a string listing the covered functions, separated by commas, in sorted order.
- Prepare a string listing the untested functions, separated by commas, in sorted order.
- Print the covered functions with the label "Tested functions:".
- Print the untested functions with the label "Untested functions:".
Solução
Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixoObrigado pelo seu feedback!
single
